The present invention relates to a method and an arrangement for reducing of or correcting for fixed pattern noise (FPN) in the image signals from an image sensor having a plurality of pixels.
Modern image sensors for recording images have a plurality of individual pixels which are constructed from partially light-sensitive electronic components. The pixels generate, in dependence on the light impinging on them, analogue image signals having analogue image signal values at any instant in time. The analogue image signal values are converted into digital image signal values in a subsequent processing stage by means of an A/D converter. The digital image signal values of all pixels represent a digital image of the recorded scene which can later be reproduced on a monitor, a printer and the like.
A known problem in such image sensors is the fixed pattern noise (FPN). This is the name given to inhomogeneities in an image, which are mainly caused by production tolerances of the individual pixels. In the production of CMOS image sensors having a logarithmic characteristic, for example, variations with regard to the threshold voltages of the logarithmic transistors typically occur. When a recorded image is viewed, the fixed-pattern noise caused by such production tolerances shows up in areas which are actually uniform (homogeneous) but exhibit a pattern which is not present in reality.
To reduce or eliminate the fixed-pattern noise, it is known to add individual correction values to the image signal values from the individual pixels. By adding negative correction values, a subtraction in a mathematical sense can also be achieved. The goal of this measure is to equalize the differences existing between the image signal values of the individual pixels caused by production tolerances by adding suitable correction values. The correction values for the image signal value of each pixel can be taken from a memory. Such an FPN correction is known, for example, from JP 5-137073 A, where the image signal values which are already digitized are corrected in accordance with this document.
As will be acknowledged, the quality of the FPN correction decisively depends on the selection and determination of the individual correction values. The problem is, therefore, to determine individual correction values suitable for a particular image sensor. Prior to the present invention, the present assignee has proceeded as follows:
Firstly, a uniform (static) correction value was set for all pixels of the image sensor. Then a homogeneous reference image was recorded by means of the image sensor. A suitable reference image is, for example, a uniformly illuminated area (known from the so-called white calibration in cameras). Since the correction values of all pixels are identical in the recording of this reference image, the image signal values of the individual pixels exactly reflect the fixed-pattern noise in this case.
In the next step, a mean value over all pixels was formed from the recorded image signal values of the reference image. This mean value was then used as a basis for a uniform target image value for all pixels. To determine the suitable individual correction values for all pixels, all correction values from a set of possible correction values were tested in a test run for all pixels. In the case of correction values having a width of 8 bits, this required 256 loop iterations per pixel. As suitable individual correction value, the correction value with which the associated image signal value came closest to the uniform target image value was selected for each pixel.
The method represents a simple possibility for determining suitable individual correction values with low demands on the hardware used. The fixed-pattern noise can be reduced considerably by means of the correction values found in this manner.
However, carrying out this method requires a considerable processing time due to the numerous loop iterations. The larger the set of possible correction values, i.e. the data width of the individual correction values, the greater the processing time. Therefore, an improvement in the FPN correction by increasing the data width of the correction values leads to even very much longer processing times.